在 CI/CD 中確保 AI 生成內容的繁體中文品質
LLM 生成的繁體中文常混用簡中詞彙，台灣讀者一眼便知。透過 zhtw 工具在 CI/CD 流程中加入自動化檢查，可將語意錯誤攔截在部署前，確保產品體驗的一致性。
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Traditional Chinese QA for AI Content in CI/CD
LLM-generated Traditional Chinese often mixes in Simplified Chinese vocabulary, and Taiwan readers spot it immediately. By adding automated checks to the CI/CD pipeline with the zhtw tool, teams can catch semantic errors before deployment and keep the product experience consistent.
混沌工程：在 AI 時代重建系統韌性
儀表板綠燈不代表系統安全。當 LLM 延遲飆升、向量資料庫狀態不一致時，傳統監控往往失效。本文探討如何從「預防」轉向「韌性驗證」，並提供在 AI 導入期建立故障注入框架的實戰指南。
Chaos Engineering: Rebuilding AI System Resilience
A green dashboard doesn’t mean the system is safe. When LLM latency spikes or vector database states become inconsistent, traditional monitoring often fails. This article explores how to shift from “prevention” to “resilience validation,” and provides a practical guide to building a fault injection framework during AI adoption.
From Context Loss to Self-Healing CI: Observations from Anthropic’s Engineering Practice
Anthropic engineers shared at a Taipei Meetup how hooks and automation can productize CI’s feedback loop, reducing waiting and manual rework.
從 Context 流失到自我修復 CI：Anthropic 的工程實戰觀察
Anthropic 工程師在臺北 Meetup 分享：如何透過 hooks 與自動化機制，把 CI 的回饋迴路產品化，減少等待與人工重工。
Design System Layered Architecture: From Preset to Platform
When Design System client needs start overlapping, a flat Preset architecture can collapse into exploding maintenance costs. This post explores how a three-layer decoupled architecture of Style, Color, and Industry turns maintenance costs from exponential growth into modular work, enabling Design Systems to scale.
Design System 分層架構：Style × Color × Industry 的組合爆炸
當 Design System 的客戶需求開始交叉，扁平的 Preset 架構會陷入維護成本爆炸的困境。本文探討如何透過 Style、Color 與 Industry 三層解耦架構，將維護成本從指數增長轉為模組化，實現設計系統的規模化。
DevOps 沒有取代 SDLC，AI 也不會
PR merge 速度翻倍，rollback 次數也跟著翻倍。AI 加速的是寫程式碼那一段,但 review 與測試的反饋網沒跟著加密。本文拆解 SDLC、DevOps、CI/CD 三層架構,看 AI 該被擺進哪一層。
Why Does AI-Written UI Always Fall a Little Short? Because Nobody Told It What Counts as Good
AI-generated UI quality is inconsistent because it lacks concrete constraints. UI Skills turns vague best practices into verifiable rules. This post analyzes its design patterns and shows you how to build a team-specific Skill constraint system.